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Summary As the experiment techniques and strategies in quantitative proteomics are improving rapidly, the corresponding algorithms and tools for protein quantification with high accuracy and precision are continuously required to be proposed. Here, we present a comprehensive and flexible tool named PANDA for proteomics data quantification. PANDA, which supports both label-free and labeled quantifications, is compatible with existing peptide identification tools and pipelines with considerable flexibility. Compared with MaxQuant on several complex datasets, PANDA was proved to be more accurate and precise with less computation time. Additionally, PANDA is an easy-to-use desktop application tool with user-friendly interfaces. Availability and implementation PANDA is freely available for download at https://sourceforge.net/projects/panda-tools/. Supplementary information Supplementary data are available at Bioinformatics online [ABSTRACT FROM AUTHOR]

Host immunity is central to the virus's spread dynamics, which is significantly influenced by vaccination and prior infection experiences. In this work, we analyzed the co-evolution of SARS-CoV-2 mutation, angiotensin-converting enzyme 2 (ACE2) receptor binding, and neutralizing antibody (NAb) responses across various variants in 822 human and mice vaccinated with different non-Omicron and Omicron vaccines is analyzed. The link between vaccine efficacy and vaccine type, dosing, and post-vaccination duration is revealed. The classification of immune protection against non-Omicron and Omicron variants is co-evolved with genetic mutations and vaccination. Additionally, a model, the Prevalence Score (P-Score) is introduced, which surpasses previous algorithm-based models in predicting the potential prevalence of new variants in vaccinated populations. The hybrid vaccination combining the wild-type (WT) inactivated vaccine with the Omicron BA.4/5 mRNA vaccine may provide broad protection against both non-Omicron variants and Omicron variants, albeit with EG.5.1 still posing a risk. In conclusion, these findings enhance understanding of population immunity variations and provide valuable insights for future vaccine development and public health strategies., (© 2024 The Author(s). Advanced Science published by Wiley‐VCH GmbH.)

The rapid development of proteomics studies has resulted in large volumes of experimental data. The emergence of big data platform provides the opportunity to handle these large amounts of data. The integrated proteome resource, iProX (https://www.iprox.cn), which was initiated in 2017, has been greatly improved with an up-to-date big data platform implemented in 2021. Here, we describe the main iProX developments since its first publication in Nucleic Acids Research in 2019. First, a hyper-converged architecture with high scalability supports the submission process. A hadoop cluster can store large amounts of proteomics datasets, and a distributed, RESTful-styled Elastic Search engine can query millions of records within one second. Also, several new features, including the Universal Spectrum Identifier (USI) mechanism proposed by ProteomeXchange, RESTful Web Service API, and a high-efficiency reanalysis pipeline, have been added to iProX for better open data sharing. By the end of August 2021, 1526 datasets had been submitted to iProX, reaching a total data volume of 92.42TB. With the implementation of the big data platform, iProX can support PB-level data storage, hundreds of billions of spectra records, and second-level latency service capabilities that meet the requirements of the fast growing field of proteomics., (© The Author(s) 2021. Published by Oxford University Press on behalf of Nucleic Acids Research.)